Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks

Solar Energy

Volumn 122, Issue , 2015, Pages 1309-1324

  Mazorra Aguiar, L ^a   Pereira, B ^a   David, M ^b   Diaz F ^a   Lauret, P ^b  

^a UNIVERSITY OF LAS PALMAS DE GRAN CANARIA   (Spain)

^b UNIVERSITÉ DE LA RÉUNION   (France)

Author keywords

Artificial Neural Networks; Satellite images; Solar forecasting; Spatio temporal analysis

Indexed keywords

BAYESIAN NETWORKS; ELECTRIC POWER TRANSMISSION NETWORKS; NEURAL NETWORKS; SATELLITES; SOLAR RADIATION;

BAYESIAN ARTIFICIAL NEURAL NETWORKS; GROUND MEASUREMENTS; PERSISTENCE MODELS; POWER SYSTEMS PLANNING; SATELLITE IMAGES; SOLAR FORECASTING; SOLAR RADIATION FORECASTING; SPATIOTEMPORAL ANALYSIS;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; FORECASTING METHOD; IRRADIANCE; POWER GENERATION; SATELLITE DATA; SATELLITE IMAGERY; SOLAR RADIATION; SPATIOTEMPORAL ANALYSIS;

CANARY ISLANDS; GRAN CANARIA; LAS PALMAS; SPAIN;

EID: 84947778908     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2015.10.041     Document Type: Article

References (43)

1. Badescu V., Gueymard C.A., Cheva S., Oprea C., Baciu M., Dumitrescu A., Iacobescu F., Milos I., Rada C. Accuracy analysis for fifty-four clear-sky solar radiation models using routine hourly global irradiance measurements in Romania. Renew. Energy 2013, 55:85-103.
2. Bird, R., Hulstrom, R., 1981. Simplified the Clear Sky Model for Direct and Diffuse Insolation on horizontal Surfaces, Technical Report No. SERI/TR-642-761, s.l.: Solar Energy Research Institute.
3. Bishop C. Neural Networks for Pattern Recognition 1995, Oxford University Press, Oxford.
4. Blanc, P., 2011. Atlas du Potentiel Solaire Photovoltaique et Thermodynamique en region PACA, Conseil General Alpes Maritimes: Centre Energetique et Procedes, MINES ParisTech/ARMINES.
5. Blanc P., Gschwind B., Lefèvre M., Wald L. The HelioClim project: surface solar irradiance data for climate applications. Remote Sens. 2011, 3(2):343-361.
6. Canada's Environment, 2015. World Ozone Monitoring Mapping. Available at: <>. http://es-ee.tor.ec.gc.ca/e/ozone/ozoneworld.htm.
7. Chatfiel C. Time Series Analysis, An Introduction 2004, s.l.: Hapman & Hall.
8. Coimbra C., Kleissl J., Marquez R. Overview of solar-forecasting methods and a metric for accuracy evaluation. Solar Energy Forecasting and Resource Assessment 2013, 171-194. Elsevier, s.l.
9. Dambreville R. Prevision du rayonnement solaire global par teledection pur la gestion de la production d'energie photovoltaique 2014, These de l'Université de Grenoble, Grenoble.
10. Dambreville R., Blanc P., Chnussot J., Boldo D. Very short term forecasting of the global horizontal irradiance using a spatio-temporal autoregressive model. Renew. Energy 2014, 72:291-300.
11. Diagne M., David M., Boland J., Schmutz N., Lauret P. Post-processing of solar irradiance forecasts from WRF model at Reunion Island. Sol. Energy 2014, 105:99-108.
12. Diagne M., David M., Lauret P., Boland J., Schmutz Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew. Sustain. Energy Rev. 2013, 27:65-76.
13. Díaz F., Montero G., Escobar J.M., Rodríguez E., Montenegro R. An adaptive solar radiation numerical model. J. Comput. Appl. Math. 2012, 236(18):4611-4622.
14. Hammer A., Heinemann D., Lorenz E., Lückehe B. Short-term forecasting of solar radiation: a statistical approach using satellite data. Sol. Energy 1999, 67(1):138-150.
15. Heinemann D., Lorenz E., Girodo M. Forecasting of solar radiation. Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale 2006, Nova Science Publishers, New York.
16. Hoff T., Perez R. Quantifying PV power output variability. Sol. Energy 2010, 84:1782-1793.
17. Hoff T., Perez R. Modeling PV fleet output variability. Sol. Energy 2012, 86:2177-2189.
18. Holben B.N., Eck T.F., Slutsker I., Tanre D., Buis J.P., Setzer A., Vermote E., Reagan J.A., Kaufman Y.J., Nakajima T., Lavenu F., Jankowiak I., Smirnov A. AERONET - a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 1998, 66:1-16.
19. Hontoria L., Aguilera J., Zufiria P. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Sol. Energy 2002, 72(5):446-2002.
20. Ineichen P. Comparison of eight clear sky broadband models against 16 independent data banks. Sol. Energy 2006, 80:468-478.
21. ITC Canarias, 2011. Mapasolarcanarias. [En línea] Available at: <>. http://mapasolar.itccanarias.org/mapasolarcanarias/.
22. Kostylev, V., Pavlovski, A., 2011. Solar Power Forecasting Performance - Toward Industry Standards. Aarhus, Denmark, October 24, s.n.
23. Lauret P., David M., Fock E., Bastide A., Riviere C. Bayesian and sensitivity analysis approaches to modeling the direct solar irradiance. Sol. Energy Eng. 2006, 128:394-405.
24. Lauret P., Fock E., Mara T. A node pruning algorithm based on the Fourier amplitude sensitivity test method. IEEE Trans. Neural 2006, 17(2):273-293.
25. Lauret P., Fock E., Randrianarivony R., Manicom-Ramsamy J. Bayesian neural network approach to short time load forecasting. Energy Convers. Manage. 2008, 49(5):1156-1166.
26. Lauret P., Voyant C., Soubdhan T., David M., Poggi P. A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. Sol. Energy 2015, 112:446-457.
27. Lefèvre M., Oumbe A., Blanc P., Espinar B., Gschwind B., Qu Z., Wald L. McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions. Atmos. Meas. Tech. 2013, 6:2403-2418.
28. Lorenz E., Heinemann D. Prediction of solar irradiance and photovoltaic power. Comprehensive Renewable Energy 2012, 239-292. Elsevier, Oxford.
29. Mackay D. Information Theory, Inference, and Learning Algorithms 2003, Cambridge University Press, Cambridge.
30. Marquez R., Pedro H., Coimbra C. Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs. Sol. Energy 2013, 92:176-188.
31. Maxwell, E., Wilcox, S., Rymes, M., 1993. Users manual for SERI QC software, assessing the quality of solar radiation data, s.l.: NREL-TP-463-5608. 1617 Cole Boulevard, Golden, CO, National Renewable Energy Laboratory.
32. Mazorra, L., Díaz, F., Montero, G., Montenegro, R., 2010. Typical Meteorological Year (TMY) evaluation for power generation in Gran Canaria Island, Spain. Valencia, s.n.
33. Mellit A., Pavan A. A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol. Energy 2010, 84:807-821.
34. Nabney I. NETLAB: Algorithms for Pattern Recognition 2002, Springer, London.
35. Penny W., Roberts S. Bayesian neural networks for classification: how useful is the evidence framework?. Neural Networks 1999, 12:877-892.
36. Perez R., Kivalov S., Schlemmer J., Hemker K., Renné D., Hoff T.E. Validation of short and medium therm operational solar radiation forecasts in the US. Sol. Energy 2010, 84:2161-2172.
37. Reno, M., Hansen, C., Stein, J., 2012. Global horizontal irradiance clear sky models: implementation and analysis, s.l.: SANDIA Report SAND2012-2389.
38. Rigollier C., Lefèvre M., Wald L. The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Sol. Energy 2004, 77:159-169.
39. Willmott C., Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 2005, 30:79-82.
40. Wittmann M., Breitkreuz H., Schroedter-Homscheidt M., Eck M. Case studies on the use of solar irradiance forecast for optimized operation strategies of solar thermal power plants. Sel. Top. Appl. Earth Observations Remote Sens. IEEE J. 2004, 1(1):18-27.
41. Younes S., Claywell R., Muneer T. Quality control of solar radiation data: present status and proposed new approaches. Energy 2005, 30:1533-1549.
42. Younes S., Muneer T. Clear-sky classification procedures and models using a world-wide data-base. Appl Energy 2007, 84:623-645.
43. Zagouras A., Pedro H., Coimbra C. On the role of lagged exogenous variables and spatio-temporal correlations in improving the accuracy of solar forecasting methods. Renew Energy 2015, 78:203-218.